The present invention relates to techniques for analyzing data, such as data defining an image, to obtain information about groupings.
Mahoney, U.S. Pat. No. 5,239,596, describes techniques for labeling pixels in an image based on near neighbor attributes. FIG. 7 shows general steps in labeling pixels according to a link relationship criterion and FIG. 8 shows general steps in iterative labeling by applying a near neighbor criterion that is independent of distance. FIGS. 14-19, discussed beginning at col. 13 line 42, show steps in labeling pixels in various ways. A discussion of linked group labeling through grouping operations begins at col. 15 line 63. Grouping operations can apply a wide variety of criteria, including proximity. As discussed at col. 17 lines 15-19, a nearest neighbor criterion selects, at each pixel, the link for which the distance to the neighbor is the minimum. As discussed at col. 17 lines 26-43, a mutuality criterion selects, at each pixel, the link that is mutual with another link, which occurs when pixels are mutual neighbors. As discussed at col. 17 line 45-col. 18 line 11, a proximity grouping criterion defines clusters of elements using local neighbor distances alone, based on the intuition that cluster boundaries are locations of abrupt change in the distance between neighbors--that is, link-lengths vary slowly within a cluster but change abruptly at the boundaries between clusters. As described at col. 18 lines 6-12, sensitivity to scale may be controlled by deselecting a link if it is longer than the product of a constant parameter times the larger of two distances, one of which is the shortest link at the same pixel, the other of which is a global distance parameter. The global distance parameter could, for example, be based on the length of the longest link in the image or the most common link length. Source code for proximity grouping begins in cols. 31 and 32, near mid-page.